Easy Statistics: Linear and NonLinear Regression
An easy introduction to Ordinary Least Squares, Logit and Probit regression and tips for regression modelling.
What you’ll learn

The theory behind linear and nonlinear regression analysis.

To be at ease with regression terminology.

The assumptions and requirements of Ordinary Least Squares (OLS) regression.

To comfortably interpret and analyse regression output from Ordinary Least Squares.

To learn and understand how Logit and Probit models work.

To learn tips and tricks around NonLinear Regression analysis.

Practical examples in Stata
Requirements

None
Description
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Three courses combined. Linear and NonLinear Regression and Regression Modelling.
Learning and applying new statistical techniques can often be a daunting experience.
“Easy Statistics” is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.
This course will focus on the concept of linear regression, nonlinear regression and regression modelling. Specifically Ordinary Least Squares, Logit and Probit Regression.
The first two parts will explain what regression is and how linear and nonliner regression works. It will examine how Ordinary Least Squares (OLS) works and how Logit and Probit models work. It will do this without any complicated equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts.
No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis.
The main learning outcomes are:
 To learn and understand the basic statistical intuition behind Ordinary Least Squares
 To be at ease with general regression terminology and the assumptions behind Ordinary Least Squares
 To be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least Squares
 To learn tips and tricks around linear regression analysis
 To learn and understand the basic statistical intuition behind nonlinear regression
 To learn and understand how Logit and Probit models work
 To be able to comfortably interpret and analyze complicated regression output from Logit and Probit regression
 To learn tips and tricks around nonlinear Regression analysis
Specific topics that will be covered are:
 What kinds of regression analysis exist
 Correlation versus causation
 Parametric and nonparametric lines of best fit
 The least squares method
 Rsquared
 Beta’s, standard errors
 Tstatistics, pvalues and confidence intervals
 Best Linear Unbiased Estimator
 The GaussMarkov assumptions
 Bias versus efficiency
 Homoskedasticity
 Collinearity
 Functional form
 Zero conditional mean
 Regression in logs
 Practical model building
 Understanding regression output
 Presenting regression output
 What kinds of nonlinear regression analysis exist
 How does nonlinear regression work?
 Why is nonlinear regression useful?
 What is Maximum Likelihood?
 The Linear Probability Model
 Logit and Probit regression
 Latent variables
 Marginal effects
 Dummy variables in Logit and Probit regression
 Goodnessoffit statistics
 Oddratios for Logit models
 Practical Logit and Probit model building in Stata
The computer software Stata will be used to demonstrate practical examples.
Regression Modelling
The third part provides useful practical tips for regression modelling.
Understanding how regression analysis works is only half the battle. There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these sessions, we will examine some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them? Each topic has a practical demonstration in Stata. Themes include:
 Fundamental of Regression Modelling – What is the Philosophy?
 Functional Form – How to Model NonLinear Relationships in a Linear Regression
 Interaction Effects – How to Use and Interpret Interaction Effects
 Using Time – Exploring Dynamics Relationships with Time Information
 Categorical Explanatory Variables – How to Code, Use and Interpret them
 Dealing with Multicollinearity – Excluding and Transforming Collinear Variables
 Dealing with Missing Data – How to See the Unseen
Who this course is for:
 Academic students of any level.
 Practitioners who require quantitative knowledge.
 Business users and managers who engage with quantitative reports.
 Government workers who are involved in policy analysis.
 Anyone who has an interest in, or needs to engage, with statistical regression.
Created By  F. Buscha 
Last Updated  11/2020 
Language  English 
Size 
2.85 GB 
https://www.udemy.com/course/easystatisticslinearandnonlinearregression/